New Technologies Has Helped Financiers to Adapt to The New Reality
COVID-19 pandemic has become an exogenous shock to the economy that significantly changed consumer behavior. The financial industry is simultaneously one of those most affected sectors by this shock and one of the leaders in both the development and implementation of new technologies. Technological leadership and the ability to shift to digital models made it possible for banks, insurance companies and other financial institutions to overcome the consequences of the coronavirus more easily concluded the participants of the Innovations in Finance session of the XI Annual International Academic Conference “Foresight and Science, Technology and Innovation Policy” October 15-26, 2021.
The financial sector is characterized by its high level of innovation, which ranges across many different areas: from novel financial products and the development and application of new technologies (such as Artificial Intelligence, AI). In order both to enable, and to capitalize upon, such innovations, banks, insurance companies, and other financial advisory businesses have been introducing upgrading skills and competences, and restructuring organizational practices. These topics were discussed at the session ‘Innovation in Finance’ chaired by Professor Ian Miles (HSE University, Russia; University of Manchester, UK), that brought together researchers and practitioners from several countries. It is noteworthy that several of the presentations focused on such topics as the appraisal and results of investment and M&A (mergers and acquisitions) decisions. While their evidence and insights provide much food for thought, they would all agree that they have just scratched the surface of a vast, and fast-moving, set of phenomena.
In the first presentation, Professor Thomas Thurner (HSE University, Russia), provided recent examples of new service offerings based on digital technologies by leading Russian banks. Thus, most of those examined in the study were found to be applying their competences, and offering new services – including striking services in education, entertainment, advanced robotics and healthcare fields. Thurner and colleagues interpreted this in the light of theories of the “reverse product cycle”, which see back-office digitalization as becoming the basis for new and improved services. In addition, the use of digital technologies was accelerated by the pandemic. As well as disrupting established behavioral routines based on face-to-face contact, forcing companies and financial institutions to reorganize their business processes, this provoked several banks to provide new services helping users to themselves better deal with the challenges of the crisis.
Technology development is a driver for organizational change that has often been seen as requiring individuals with “T-shaped skills” (enabling effective communication and collaboration across professions and areas of expertise. The second presentation built on this idea, to demonstrate that cross-functional T-shaped teams have become a necessity for AI and big data adoption to be employed in financial advisory services, providing expertise on investment opportunities and risks. The reality is that significant development of such technologies makes them so complicated that it becomes extremely difficult for investment professionals to pick them up without a formal education and relevant experience. Larry Cao, Senior Director at CFA Institute (USA), reported work suggesting that they must step forward from a traditional, but decreasingly effective, functional structure, towards the development of T-shaped teams, in which innovation facilitators and knowledge engineers facilitate collaboration between data scientist and financial analysts. The development of such a team may include three key steps. The first one is to identify those key areas of the investment business that need most help from data science, and to develop the technology roadmap for the AI and big data applications that deliver on those objectives. Second, since this new operational structure with T-shape teams needs new processes to work, it is necessary to create effective processes that help both investment and technology professionals collaborate. Finally, for the approach to gain traction, it may be a valuable strategy to find and scope “quick wins”, and balance the impact on the investment process, the technical feasibility, and related time/resource commitment.
Businesses may seek to improve their technological competencies by training or recruiting staff, or by sourcing externally generated knowledge – and one way of doing the latter is to acquire another company that have already developed the relevant knowledge base. However, despite the popularity of M&A strategies, there are many examples of them failing to achieve expected returns. Yury Dranev (HSE University, Russia) warned that such strategies may encounter difficulties of various kinds; among these he pointed to widely different knowledge bases, distinctive corporate cultures, and disruption of established routines. A study was reported employing data envelopment analysis to examine the efficiency of technology M&A deals across a large sample of internationally listed companies from 2008 to 2017. Merged firms displayed a “substitution effect”, with the efficiency of the acquisition decreasing when the technological strength of the acquirer was higher.
The fourth presentation also concerned M&A deals, in this case considering corporate governance issues as influencing how investors valued M&A. Traditionally corporate governance is based on external mechanisms like Board of Directors, but Gael Imad’Eddine (University of Lille, France) presented a new approach developed by Acharya, Myers and Rajan, and applied this to data on market reactions to M&A announcements. The new approach focuses on internal governance, and assesses this in terms of two key elements - the existence of a succession plan, and the balanced distribution of power between the CEO and the potential successor. Such internal governance was found to be positively associated with the creation of value during M&A deals; a hump-shaped distribution emerged when value-creation (indexed by market reaction) was plotted against the distribution of balance of power between incumbent and successor. Furthermore this effect is stronger when the external governance is weaker, and where the successor worked under promotion-based incentives. The argument is that internal governance and the promotion-based system is seen to give more incentives to the potential successor to invest in firm-specific human capital, and to increase current cash flows, making the M&A process more attractive to the market.
These interesting presentations ranged from large-scale data analyses to insightful deductions from case study engagement; from a focus on empirical trends to attention to emerging practices. The chair initiated a discussion of how, and how far the different lines of work could mutually benefit each other. Among the suggestions from this discussion were the point that practitioners generally have to be alert both to specificities of the precise context in which they work, and to the broader forces and trends that these are situated within. In contrast, academic research tends to focus on highly specific problems and processes, and to abstract away from local circumstances. The two are in many ways complementary, but it will be rarely the case that the results of one sort of work are directly translatable and applicable in the other field. Still, we might hope that the onward march of data analytics, and the growing scope for using near real-time data, may allow for more cross-over in the years ahead.
Contributors to this session made a number of final comments, which are reproduced below. (Veronika Belousova coauthored the paper presented by Thomas Thurner, who could not join the discussion due to being in transit at the time of the sessions)
This session on innovation in financial services has pointed out key drivers for creating value for both enterprises and financial institutions, that involve linking the benefits which technological advance can provide for decision making, with the tacit knowledge, which professionals bring to the cross-functional teams. Definitely, there is a balance achieved between the recent penetration of artificial intelligence and the competences of humans for keeping the process under control - especially when facing the pandemic and the associated need to coordinate processes effectively in a distributed manner. This suggests that we might also expect changes in corporate practices for managing investor’s expectations to emerge. This should lead CEOs to take account of social responsibility (especially in the context of climate change), which will definitely have a great impact on daily operations - and where M&As can be used as a route to acquire technology and local knowledge, human and social capital.
I appreciate the opportunity to share our research on the ideal organizational structure for AI and big data adoption efforts at investment firms. Although this is an empirical question, from an organizational behavioral theory perspective one can also see the role organization and processes play in the most profound innovation efforts financial institutions have event experienced. I am glad the conference organizer found that angle interesting. The other speakers at the session also had interesting perspectives: the services Russian banks offered under Covid are absolutely unique and it remains interesting to see what long term implications this experience will have on the banking industry there. The M&A approach to talent development proved less efficient, much like what valuation professors often say about M&A: “just don’t do it”; although it is interesting to see that better governance may come to the rescue. These are fascinating results that are worthy of further research and attention.
Larry Cao
CFA
This session was rich in teachings, and we were lucky to have also practitioners’ inputs. The issue of AI in finance is highly topical. It shapes the investment industry with automated trading, but this is not the only domain. We will face many challenges soon: one of the biggest is obviously climate change. We need to push innovation further to help shape a system where investments are channeled toward decarbonization of the economy. AI could be a great tool for that, and since AI systems are programmed by humans, how they will help is up to us. The T-shaped teams could be a powerful organizational form designed to foster innovation to answer the decarbonization of the economy. It would be valuable, too, to develop metrics to measure the efficiency of M&As toward this goal. The DEA methodology is interesting: this could be calibrated to assess the impact of M&A and M&A conditions on the carbon foot-print of the firms involved.
Gael Imad’Eddine
University of Lille, France
‘Innovation in Finance' session (in Russian)
Source: HSE ISSEK website
Veronika Belousova
HSE University, Russia